Photodissociation of alkyl iodides in solution: Substituent effects on the early-time dynamics

Resonance Raman spectra, including absolute scattering cross sections, have been measured for ethyl, isopropyl, and tert-butyl iodides in cyclohexane solution at seven to ten wavelengths between 303 and 200 nm. Spectra of fully deuterated ethyl iodide have also been obtained at five wavelengths. Spectra excited in the 300-250 nm region, on resonance with the directly dissociative A state, are dominated by long overtone progressions in the nominal C-I stretching mode near 500 cm-1. In all three molecules the fundamental of the C-I stretch is unexpectedly weak relative to the overtones when excited near the peak of the A band. This is shown to arise from interference between the A-state resonant part of the fundamental Raman amplitude and preresonant contributions from higher electronic states. In addition to the C-I stretching activity, A-state excitation generates significant intensity in fundamentals, overtones, and combination bands of modes nominally assigned as bending and CC stretching vibrations, suggesting a multidimensional character to the reaction coordinate. The absorption spectra and A-state resonant Raman intensities are modeled successfully through wave-packet propagation on a multidimensional locally harmonic potential with a preresonant contribution to the fundamental intensities included. The short-time photodissociation dynamics are then examined by using the normal-mode coefficients to convert the wave-packet motion from dimensionless normal coordinates into internal coordinates. It is found that while the dominant motion during the first 10 fs involves stretching of the C-I bond, other stretching and bending motions are also involved, although the precision of these conclusions for isopropyl and tert-butyl iodides is limited by the indeterminacy in the signs of the normal-mode displacements obtained from the intensity analysis. Comparison of the results for normal and perdeuterated ethyl iodide is used to resolve most of the sign indeterminacies for this molecule. The present results are compared and contrasted to conclusions of previous studies of energy partitioning in the vapor-phase photodissociation. © 1991 American Institute of Physics.published_or_final_versio

Altered low-frequency brain rhythms precede changes in gamma power during tauopathy

Neurodegenerative disorders are associated with widespread disruption to brain activity and brain rhythms. Some disorders are linked to dysfunction of the membrane-associated protein Tau. Here, we ask how brain rhythms are affected in rTg4510 mouse model of tauopathy, at an early stage of tauopathy (5 months), and at a more advanced stage (8 months). We measured brain rhythms in primary visual cortex in presence or absence of visual stimulation, while monitoring pupil diameter and locomotion to establish behavioral state. At 5 months, we found increased low-frequency rhythms during resting state in tauopathic animals, associated with periods of abnormally increased neural synchronization. At 8 months, this increase in low-frequency rhythms was accompanied by a reduction of power in the gamma range. Our results therefore show that slower rhythms are impaired earlier than gamma rhythms in this model of tauopathy, and suggest that electrophysiological measurements can track the progression of tauopathic neurodegeneration

Autonomous Detection of the Loss of a Wing for Underwater Gliders

Over the past five years, two of the Slocum underwater gliders operated by the UK National Oceanography Centre have lost a wing mid-mission without the pilot being aware of the problem until the point of vehicle retrieval. In this study, the steady-state data collected by gliders during the two deployments has been analysed to develop a fault detection system. From the data analysis, it is clear that the loss of the wing was a sudden event for both gliders. The main changes to the system dynamics associated with the event are an increase in the positive buoyancy of the glider and the occurrence of a roll angle on the side of the lost wing, with significant difference between dives and climbs. Hence, a simple effective system for the detection of the wing loss has been designed using the roll angle. Since sensors are known to fail and the roll sensor is non-critical to the operation of the glider, a back-up diagnostics system has been developed based on the dynamic model of the vehicle, capturing the change in buoyancy. Both systems are able to correctly detect the loss of the wing and notify pilots, who can re-plan missions to safely recover the vehicle

Plasticity in visual cortex is disrupted in a mouse model of tauopathy

Alzheimer’s disease and other dementias are thought to underlie a progressive impairment of neural plasticity. Previous work in mouse models of Alzheimer’s disease shows pronounced changes in artificially-induced plasticity in hippocampus, perirhinal and prefrontal cortex. However, it is not known how degeneration disrupts intrinsic forms of brain plasticity. Here we characterised the impact of tauopathy on a simple form of intrinsic plasticity in the visual system, which allowed us to track plasticity at both long (days) and short (minutes) timescales. We studied rTg4510 transgenic mice at early stages of tauopathy (5 months) and a more advanced stage (8 months). We recorded local field potentials in the primary visual cortex while animals were repeatedly exposed to a stimulus over 9 days. We found that both short- and long-term visual plasticity were already disrupted at early stages of tauopathy, and further reduced in older animals, such that it was abolished in mice expressing mutant tau. Additionally, visually evoked behaviours were disrupted in both younger and older mice expressing mutant tau. Our results show that visual cortical plasticity and visually evoked behaviours are disrupted in the rTg4510 model of tauopathy. This simple measure of plasticity may help understand how tauopathy disrupts neural circuits, and offers a translatable platform for detection and tracking of the disease

A remote anomaly detection system for Slocum underwater gliders

Marine Autonomous Systems (MAS) operating at sea beyond visual line of sight need to be self-reliant, as any malfunction could lead to loss or pose a risk to other sea users. In the absence of fully automated on-board control and fault detection tools, MAS are piloted and monitored by experts, resulting in high operational costs and limiting the scale of observational fleets that can be deployed simultaneously. Hence, an effective anomaly detection system is fundamental to increase fleet capacity and reliability. In this study, an on-line, remote fault detection system is developed for underwater gliders. Two alternative methods are analysed using time series data: feedforward deep neural networks estimating the glider’s vertical velocity and an autoencoder. The systems are trained using field data from four baseline deployments of Slocum gliders and tested on six deployments of vehicles suffering from adverse behaviour. The methods are able to successfully detect a range of anomalies in the near real time data streams, whilst being able to generalise to different glider configurations. The autoencoder’s error in reconstructing the original signals is the clearest indicator of anomalies. Thus, the autoencoder is a prime candidate to be included into an all-encompassing condition monitoring system for MAS

A Marine Growth Detection System for Underwater Gliders

Marine growth has been observed to cause a drop in the horizontal and vertical velocities of underwater gliders, thus making them unresponsive and needing immediate recovery. Currently, no strategies exist to correctly identify the onset of marine growth for gliders and only limited data sets of biofouled hulls exist. Here, a field test has been conducted to first investigate the impact of marine growth on the dynamics and power consumption of underwater gliders and then design an anomaly detection system for high levels of biofouling. A Slocum glider was deployed first for eight days with drag stimulators to imitate severe biofouling; then, the vehicle was redeployed with no additions to the hull for further 20 days. The mimicked biofouling caused a speed reduction due to a significant increase in drag. Additionally, the lower speed causes the steady-state flight stage to last longer and the rudder to become less responsive; hence, marine growth results in a shortening of deployment duration through an increase in power consumption. As actual biofouling due to p. pollicipes occurred during the second deployment, it is possible to develop and test a system that successfully detects and identifies high levels of marine growth on the glider, blending model- and data-based solutions using steady-state flight data. The system will greatly help pilots replan missions to safely recover the vehicle if significant biofouling is detected

Unsupervised anomaly detection for underwater gliders using generative adversarial networks

An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an automated fault detection system remains challenging for several reasons including limited data transmission via satellite services. Currently, most anomaly detection for marine autonomous systems, such as underwater gliders, rely on intensive analysis by pilots. This study proposes an unsupervised anomaly detection system using bidirectional generative adversarial networks guided by assistive hints for marine autonomous systems with time series data collected by multiple sensors. In this study, the anomaly detection system for a fleet of underwater gliders is trained on two healthy deployment datasets and tested on other nine deployment datasets collected by a selection of vehicles operating in a range of locations and environmental conditions. The system is successfully applied to detect anomalies in the nine test deployments, which include several different types of anomalies as well as healthy behaviour. Also, a sensitivity study of the data decimation settings suggests the proposed system is robust for Near Real-Time anomaly detection for underwater gliders

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

This is the final verison. Available from American Geophysical Union (AGU) via the DOI in this record.The K83 observational data are available from AmeriFlux (ameriflux.lbl.gov), NCEP Reanalysis data provided by NOAA/ESRL/PSD, Boulder, Colorado, USA, from the http://www.cdc.noaa.gov/ website. Model code and output is stored at GitLab (gitlab.com). This project is password protected, and the password can be obtained from the corresponding author at [email protected] upon request.Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the “fate of water,” or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.National Aeronautics and Space Administration (NASA)National Science Foundation (NSF)National Science Foundation (NSF)U.S. Department of Energy (DOE

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference